Papers with ASR system
Neural Speech Translation using Lattice Transformations and Graph Networks (D19-53)
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| Challenge: | Existing work on end-to-end systems bypass the need for intermediate representations, but this approach is limited in practical applications. |
| Approach: | They propose a lattice-tosequence model which uses lattics as encoders and graph networks to address two problems by applying latticae transformations and a neural model. |
| Outcome: | The proposed model beats pipeline approaches while being orders of magnitude faster than previous work. |
SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale (2022.emnlp-industry)
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Raphael Tang, Karun Kumar, Gefei Yang, Akshat Pandey, Yajie Mao, Vladislav Belyaev, Madhuri Emmadi, Craig Murray, Ferhan Ture, Jimmy Lin
| Challenge: | End-to-end automatic speech recognition systems require thousands of hours of manual annotation and heavyweight computation to perform inference. |
| Approach: | They propose to use a third-party ASR system as a weak supervision source and labeling functions derived from implicit user feedback to reduce human labor. |
| Outcome: | The proposed system improves word-error rate and speed up 600% over third-party ASR. |
Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation (2023.acl-long)
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| Challenge: | Using self-training or text-to-speech (TTS) to improve low-resource ASR performance is costly and can lead to catastrophic forgetting. |
| Approach: | They examine whether data augmentation techniques could help improve low-resource ASR performance . they use self-training to generate transcriptions, which are combined with original data to train new system . |
| Outcome: | The proposed approach yields a 20.5% reduction in WER compared to a system trained on 24 minutes of manually transcribed speech. |
The Norwegian Parliamentary Speech Corpus (2022.lrec-1)
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| Challenge: | the dataset contains recordings of meetings at the Norwegian parliament . it is the first publicly available dataset containing unscripted, Norwegian speech . |
| Approach: | the Norwegian Parliamentary Speech Corpus is a publicly available speech dataset . it contains recordings of meetings from the Norwegian parliament with orthographic transcriptions . the dataset is intended to fill a gap in the available unscripted speech data . |
| Outcome: | the dataset contains recordings of meetings at the Norwegian parliament with orthographic transcriptions in Norwegian Bokml and Norwegian Nynorsk. |
Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent? (2022.emnlp-main)
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| Challenge: | ASR systems are often unable to recognize speech due to generic datasets and open-vocabulary modeling. |
| Approach: | They propose to incorporate a robot’s visual information into an ASR system and improve the recognition of a spoken utterance containing a visible entity. |
| Outcome: | The proposed method achieves a 59% relative reduction in WER from an unmodified ASR system. |
End-to-end ASR to jointly predict transcriptions and linguistic annotations (2021.naacl-main)
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| Challenge: | Existing models generate audio transcripts by sequentially producing likely graphemes, or multi-graphemic units, from which lexical items of a language can be recovered. |
| Approach: | They propose a Transformer-based sequence-to-sequence model for automatic speech recognition that can produce high-quality transcriptions and linguistic annotations. |
| Outcome: | The proposed model can produce high-quality transcriptions and linguistic annotations on Japanese and English audio datasets. |
RED-ACE: Robust Error Detection for ASR using Confidence Embeddings (2022.emnlp-main)
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| Challenge: | ASR Error Detection (AED) models post-process the output of Automatic Speech Recognition systems, in order to detect transcription errors. |
| Approach: | They propose to use ASR model's word-level confidence scores to combine ASR models with transcribed text to improve AED performance. |
| Outcome: | The proposed models combine the confidence scores and transcribed text into a contextualized representation. |
End-to-End Speech Recognition and Disfluency Removal (2020.findings-emnlp)
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| Challenge: | Disfluency detection is usually an intermediate step between an automatic speech recognition system and a downstream task. |
| Approach: | They propose to train models to directly map disfluent speech into fluent transcripts without relying on a separate disfluency detection model. |
| Outcome: | The proposed models learn to generate fluent transcripts, but their performance is slightly worse than a baseline pipeline approach consisting of an ASR system and a specialized disfluency detection model. |
CB-Whisper: Contextual Biasing Whisper Using Open-Vocabulary Keyword-Spotting (2024.lrec-main)
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Yuang Li, Yinglu Li, Min Zhang, Chang Su, Jiawei Yu, Mengyao Piao, Xiaosong Qiao, Miaomiao Ma, Yanqing Zhao, Hao Yang
| Challenge: | End-to-end automatic speech recognition systems struggle to recognize rare name entities such as personal names, organizations and terminologies that are not frequently encountered in the training data. |
| Approach: | They propose a convolutional neural network-based ASR system that performs open-vocabulary keyword-spotting before the decoder to match the features between the entities and the utterances. |
| Outcome: | The proposed system significantly improves mixed-error-rate (MER) and entity recall compared to the original Whisper model on three internal datasets and two publicly available datasets. |
Error-preserving Automatic Speech Recognition of Young English Learners’ Language (2024.acl-long)
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| Challenge: | State-of-the-art speech recognition models are often trained on adult read-aloud data by native speakers and do not transfer well to young language learners’ speech. |
| Approach: | They propose to use an automated speech recognition module to train language learners' speaking skills on spontaneous speech by young language learners. |
| Outcome: | The proposed model improves on 85 hours of English audio spoken by Swiss learners and preserves their mistakes. |
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition (2021.findings-emnlp)
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Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu
| Challenge: | Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence. |
| Approach: | They propose a fast correction model that takes multiple ASR candidates as input for better correction accuracy. |
| Outcome: | The proposed model can reduce the word error rate (WER) with multiple candidates by 3.2% and 2.6%. |
Beyond Common Words: Enhancing ASR Cross-Lingual Proper Noun Recognition Using Large Language Models (2024.findings-emnlp)
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| Challenge: | In this work, we address the challenge of cross-lingual proper noun recognition in automatic speech recognition systems where proper nodes in an utterance may originate from a language different from the language in which the ASR system is trained. |
| Approach: | They propose a dictionary-based method to correct ASR predictions in a large language model . |
| Outcome: | The proposed method significantly reduces word error rates across cross-lingual proper noun recognition tasks involving three secondary languages. |
Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative Training (D19-1)
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| Challenge: | Code-switching (CS) is a linguistic phenomenon defined as "the alternation of two languages within a single discourse, sentence or constituent." |
| Approach: | They propose an ASR-motivated evaluation setup which is decoupled from an ASL system and the choice of vocabulary . they propose a discriminative training approach which works better than generative language modeling . |
| Outcome: | The proposed evaluation setup is better than generative language modeling, the authors show . the proposed setup is decoupled from an ASR system and the choice of vocabulary . |
Pronunciation Variants and ASR of Colloquial Speech: A Case Study on Czech (L18-1)
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| Challenge: | a standard speech recognition system uses a pronunciation component that maps tokens in the transcripts to their phonetic representations. |
| Approach: | They propose to use a pronunciation dictionary to map tokens in speech transcripts to phonetic representations. |
| Outcome: | The proposed pronunciation dictionary performs better than a standard rule-based pronunciation component. |
Discovering Canonical Indian English Accents: A Crowdsourcing-based Approach (L18-1)
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| Challenge: | Automated Speech Recognition systems degrade in performance when recognizing accents that are different from the ones in training data. |
| Approach: | They propose to adapt Acoustic Models that are trained on one accent to a target accent by using a small amount of speech data in the target accent. |
| Outcome: | The proposed model can be used to identify accents in Indian English and other languages. |
Open ASR for Icelandic: Resources and a Baseline System (L18-1)
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| Challenge: | Existing language resources are not sufficient for less-resourced languages, but a system with sufficient resources is needed. |
| Approach: | They describe available language resources and their preparation for use in a large vocabulary speech recognition system for Icelandic. |
| Outcome: | The proposed system improves on acoustic training sets and a speech corpus with a pronunciation dictionary. |
Simulating ASR errors for training SLU systems (L18-1)
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| Challenge: | Existing methods to simulate automatic speech recognition errors from manual transcriptions are not available during training of the SLU model. |
| Approach: | They propose to use acoustic and linguistic word embeddings to define a similarity measure between words to predict ASR confusions. |
| Outcome: | The proposed method significantly improves the performance of spoken language understanding systems. |
ASR for Documenting Acutely Under-Resourced Indigenous Languages (L18-1)
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| Challenge: | Automatic speech recognition (ASR) has not been widely explored as a tool for documenting endangered languages. |
| Approach: | They propose to use automatic speech recognition (ASR) to bootstrap new data to improve the acoustic model. |
| Outcome: | The proposed system improves the model for a polysynthetic language with few audio and text resources. |
Preparation of Bangla Speech Corpus from Publicly Available Audio & Text (2020.lrec-1)
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Shafayat Ahmed, Nafis Sadeq, Sudipta Saha Shubha, Md. Nahidul Islam, Muhammad Abdullah Adnan, Mohammad Zuberul Islam
| Challenge: | Automated speech recognition systems require large annotated speech corpus for training. |
| Approach: | They propose to use publicly available Bangla audiobooks and TV news recordings as input to prepare a large speech corpus with reasonable confidence. |
| Outcome: | The proposed algorithm outperforms the existing speech corpus and the existing corpus with speaker diarization and gender detection. |
Multimodal In-context Learning for ASR of Low-resource Languages (2026.findings-acl)
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| Challenge: | In-context learning with large language models addresses this limitation, but prior work focuses on high-resource languages covered during training and text-only settings. |
| Approach: | They propose to use multimodal ICL to learn unseen languages with multimodal learning to improve ASR in large language models. |
| Outcome: | The proposed model outperforms existing models on unseen languages with multimodal ICL (MICL) and cross-lingual transfer learning matches or outperformed models without using target-language data. |
Using Speech Technology to Test Theories of Phonetic and Phonological Typology (2024.lrec-main)
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| Challenge: | acoustic studies show that obstruents in European Portuguese have different voicing profiles than their Romance relatives. |
| Approach: | They propose to use speech technology to test phonetic typology in European Portuguese . they use acoustic phone models to force align different phone models for obstruents . |
| Outcome: | The proposed method supports previous accounts that European Portuguese is diverging from the traditional voicing system known for Romance languages towards a hybrid system where stops and fricatives are specified for different voicing features. |